code: def _fit_and_score_one(
      index, estimator, X, y, scorer, train, test, verbose, fit_params
  ):
      """Fit and score an estimator with one feature left out"""
      estimator_ = _clone(estimator)
      X_train, y_train = _safe_split(
          estimator, _drop_col(X, index), y, train
      )
      X_test, y_test = _safe_split(
          estimator, _drop_col(X, index), y, test, train
      )
      estimator_.fit(X_train, y_train, **fit_params)
      return scorer(estimator_, X_test, y_test)
  
imports: import numpy
  from sklearn.base import BaseEstimator, ClassifierMixin, is_classifier
  from sklearn.utils.metaestimators import if_delegate_has_method
  from sklearn.utils import check_X_y, safe_sqr
  from sklearn.model_selection import check_cv
  from sklearn.metrics import check_scoring
  from joblib import Parallel, delayed
  from itertools import product
  from scipy.sparse import issparse
  from .validation import _check_is_fitted
  from .base import _parse_partitions, _clone
  from .utils import _safe_split
label_desc: Use image node_id 2 for calling a global function with example usage: _fit_and_score_one(index, estimator, X, y, scorer, train, test, verbose, fit_params) and returns: scorer